Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

894
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
894
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

171
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
171
Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

863
Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the...
863
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

197
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
197
Angle of Twist: Problem Solving01:13

Angle of Twist: Problem Solving

556
An electric motor applies a torque of 700 N·m to an aluminum shaft, triggering a stable rotation. Two pulleys, B and C, are subjected to torques of 300 N·m and 400 N·m, respectively. The modulus of rigidity is provided as 25 GPa. With the knowledge of the length and diameter of each segment, the twist angle between the two pulleys can be computed. First, a section cut is made between pulleys B and C, and the cut cross-section is analyzed using a free-body diagram. Given that the torque...
556
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

238
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
238

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Portable low-coherence digital holographic microscope.

Applied optics·2025
Same author

Two-dimensional phase unwrapping based on Fourier transforms and the Yukawa potential spectrum.

Journal of the Optical Society of America. A, Optics, image science, and vision·2023
Same author

Detection of Depression-Related Tweets in Mexico Using Crosslingual Schemes and Knowledge Distillation.

Healthcare (Basel, Switzerland)·2023
Same author

Panoramic reconstruction of quasi-cylindrical objects with digital holography and a conical mirror.

Optics letters·2021
Same author

Species composition and chemical characterization of Sargassum influx at six different locations along the Mexican Caribbean coast.

The Science of the total environment·2021
Same author

Parallel implementations to accelerate the autofocus process in microscopy applications.

Journal of medical imaging (Bellingham, Wash.)·2020

Related Experiment Video

Updated: Nov 14, 2025

Author Spotlight: 3D Movement Assessment of Maxillary Posterior Teeth in Clear Aligner Treatment
07:32

Author Spotlight: 3D Movement Assessment of Maxillary Posterior Teeth in Clear Aligner Treatment

Published on: February 23, 2024

1.5K

Phase-unwrapping method based on local polynomial models and a maximum a posteriori model correction.

Alejandro Téllez-Quiñones, Juan C Valdiviezo-N, Adán Salazar-Garibay

    Applied Optics
    |March 10, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new phase-unwrapping method using maximum a posteriori estimation for high-order polynomial models. It improves upon existing Kalman filter approaches for noisy phase data.

    More Related Videos

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
    13:44

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

    Published on: August 30, 2013

    43.3K
    Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM
    11:57

    Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM

    Published on: December 1, 2016

    11.0K

    Related Experiment Videos

    Last Updated: Nov 14, 2025

    Author Spotlight: 3D Movement Assessment of Maxillary Posterior Teeth in Clear Aligner Treatment
    07:32

    Author Spotlight: 3D Movement Assessment of Maxillary Posterior Teeth in Clear Aligner Treatment

    Published on: February 23, 2024

    1.5K
    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
    13:44

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

    Published on: August 30, 2013

    43.3K
    Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM
    11:57

    Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM

    Published on: December 1, 2016

    11.0K

    Area of Science:

    • Optics
    • Signal Processing
    • Image Analysis

    Background:

    • Local polynomial approximations are used in phase-unwrapping algorithms.
    • Existing methods using Kalman filters are limited to first-order polynomial models.
    • Previous research explored higher-order polynomial models for phase unwrapping.

    Purpose of the Study:

    • To extend phase-unwrapping methodologies to higher-order polynomial models.
    • To propose a new approach based on maximum a posteriori estimation.
    • To address limitations of existing Kalman filter-based methods for low signal-to-noise ratios.

    Main Methods:

    • Developed a maximum a posteriori (MAP) estimation methodology.
    • Utilized difference vectors of coefficients as the state space for high-order models.
    • Proposed specific estimations for covariance and noise covariance matrices.

    Main Results:

    • Successfully reconstructed phase with synthetic and real data.
    • Demonstrated an effective approach for phase unwrapping with higher-order models.
    • Provided a robust method for estimating coefficient vectors in noisy conditions.

    Conclusions:

    • The proposed MAP estimation offers an effective alternative to Kalman filters for high-order phase unwrapping.
    • The method is suitable for handling low signal-to-noise ratios in wrapped phase data.
    • This research advances phase-unwrapping techniques for complex phase models.